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Hybrid centralized/decentralized control of a network of bacteria-based bio-hybrid microrobots

  • Eric J. Leaman
  • Brian Q. Geuther
  • Bahareh BehkamEmail author
Research Paper
  • 70 Downloads

Abstract

Engineering microrobotic systems using a bio-hybrid approach that couples synthetic micro/nanoscale components with live cells is a powerful approach to address key shortcomings of micro/nanotechnology such as providing an on-board power source, communication, and decentralized control. In the last decade, a number of centralized control strategies based on native biological mechanisms have been demonstrated; however, decentralized cooperative control of a swarm of bio-hybrid microrobots has rarely been explored. In this work, we use our previously developed agent-based computational model to explore the utility of integrating synthetic biology with bio-hybrid microrobotics with the goal of developing strategies for robust decentralized control of bio-hybrid microrobotic systems. To this end, our Nanoscale Bacteria-Enabled Autonomous Delivery System (NanoBEADS), wherein each agent is comprised of an Escherichia coli bacterium conjugated with an ensemble of nanoparticles, was used as a model system. We imparted bacteria with engineered biological circuits to facilitate agent-agent communication and enable predictable and robust cooperative control of a network of NanoBEADS agents. We developed a hybrid control strategy wherein a centralized chemotaxis-based control scheme is used to direct migration, and a decentralized quorum sensing (QS)-based control scheme enables the agents to independently coordinate a cooperative behavior (fluorescent protein expression). We then computationally analyzed the role of bacterial subsystem properties and spatiotemporal changes in population organization in the emergent behavior (i.e., the activation time of the QS response) of the NanoBEADS. We show that the spatial changes in NanoBEADS agents’ distribution, in response to the centralized control input, and the mass transport boundary conditions are critical factors in determining the dynamics as well as the sensitivity and robustness of the QS response. We analyze the stark differences in feasible QS parameter domains under dissimilar chemoattractant gradients and show that the flow boundary conditions may greatly influence system function. Altogether, we show that a bacteria-based bio-hybrid system must be designed with careful consideration of growth rate, chemotaxis properties, QS properties, and the environment initial and boundary conditions in the context of its target application. This modeling framework can serve as an insightful tool for the predictive design of bio-hybrid microrobotic swarms with a tunable and robust response.

Keywords

Bio-hybrid microrobots Swimming bacteria Distributed network Cooperative control Chemotaxis Quorum sensing 

Notes

Acknowledgements

The authors wish to thank Pedro Ivo Guimarães Braga da Silva and Prof. Ryan Senger of Virginia Tech (VT) for assistance with the construction of genetic circuits, Prof. Birgit Scharf of VT for providing the motile E. coli MG1655, and former lab member Dr. SeungBeum Suh for assistance with C++ coding. This work was supported in part by the National Science Foundation (IIS-117519 and CAREER award, CBET-1454226).

References

  1. 1.
    Leaman EJ, Geuther BQ, Behkam B (2018) Hybrid Centralized/Decentralized Control of Bacteria-based Bio-hybrid Microrobots. In: 2018 Proceedings of the International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS). pp 5–10Google Scholar
  2. 2.
    Ricotti L, Trimmer B, Feinberg AW et al (2017) Biohybrid actuators for robotics: a review of devices actuated by living cells. Sci Robot 2:1–18.  https://doi.org/10.1126/scirobotics.aaq0495
  3. 3.
    Gest H (1995) Phototaxis and other sensory phenomena in purple photosynthetic bacteria. FEMS Microbiol Rev 16:287–294.  https://doi.org/10.1111/j.1574-6976.1995.tb00176.x CrossRefGoogle Scholar
  4. 4.
    Adler J, Shi W (1988) Galvanotaxis in bacteria. Cold Spring Harb Symp Quant Biol 53:23–25.  https://doi.org/10.1101/SQB.1988.053.01.006 CrossRefGoogle Scholar
  5. 5.
    Alexandre G, Greer-Phillips S, Zhulin IB (2004) Ecological role of energy taxis in microorganisms. FEMS Microbiol Rev 28:113–126.  https://doi.org/10.1016/j.femsre.2003.10.003 CrossRefGoogle Scholar
  6. 6.
    Paster E, Ryu WS (2008) The thermal impulse response of Escherichia coli. Proc Natl Acad Sci U S A 105:5373–5377.  https://doi.org/10.1073/pnas.0709903105 CrossRefGoogle Scholar
  7. 7.
    Blakemore RP (1982) Magnetotactic bacteria. Annu Rev Microbiol 36:217–238CrossRefGoogle Scholar
  8. 8.
    Marcos FHC, Powers TR, Stocker R (2012) Bacterial rheotaxis. Proc Natl Acad Sci 109:4780–4785.  https://doi.org/10.1073/pnas.1120955109 CrossRefGoogle Scholar
  9. 9.
    Wadhams GH, Armitage JP (2004) Making sense of it all: bacterial chemotaxis. Nat Rev Mol Cell Biol 5:1024–1037.  https://doi.org/10.1038/nrm1524 CrossRefGoogle Scholar
  10. 10.
    Alapan Y, Yasa O, Sitti M et al (2019) Mobile microrobots for active therapeutic delivery. Adv Ther 2:1800064.  https://doi.org/10.1002/adtp.201800064 CrossRefGoogle Scholar
  11. 11.
    Martel S, Tremblay CC, Ngakeng S, Langlois G (2006) Controlled manipulation and actuation of micro-objects with magnetotactic bacteria. Appl Phys Lett 89:3–6.  https://doi.org/10.1063/1.2402221 CrossRefGoogle Scholar
  12. 12.
    Akin D, Sturgis J, Ragheb K et al (2007) Bacteria-mediated delivery of nanoparticles and cargo into cells. Nat Nanotechnol 2:441–449.  https://doi.org/10.1038/nnano.2007.149 CrossRefGoogle Scholar
  13. 13.
    Behkam B, Sitti M (2007) Bacterial flagella-based propulsion and on/off motion control of microscale objects. Appl Phys Lett 90:19–22.  https://doi.org/10.1063/1.2431454 CrossRefGoogle Scholar
  14. 14.
    Martel S, Mohammadi M (2010) Using a swarm of self-propelled natural microrobots in the form of flagellated bacteria to perform complex micro-assembly tasks. Proc - IEEE Int Conf robot autom 500–505.  https://doi.org/10.1109/ROBOT.2010.5509752
  15. 15.
    Felfoul O, Mohammadi M, Taherkhani S et al (2016) Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions. Nat Nanotechnol 11:941–947.  https://doi.org/10.1038/nnano.2016.137 CrossRefGoogle Scholar
  16. 16.
    Yasa O, Sitti M, Alapan Y et al (2018) Soft erythrocyte-based bacterial microswimmers for cargo delivery. Sci Robot 3:eaar4423.  https://doi.org/10.1126/scirobotics.aar4423 CrossRefGoogle Scholar
  17. 17.
    Yasa O, Erkoc P, Alapan Y, Sitti M (2018) Microalga-powered microswimmers toward active cargo delivery. Adv Mater 30:1–10.  https://doi.org/10.1002/adma.201804130 CrossRefGoogle Scholar
  18. 18.
    Steager E, Kim C-B, Patel J et al (2007) Control of microfabricated structures powered by flagellated bacteria using phototaxis. Appl Phys Lett 90:263901.  https://doi.org/10.1063/1.2752721 CrossRefGoogle Scholar
  19. 19.
    Traoré MA, Sahari A, Behkam B (2011) Computational and experimental study of chemotaxis of an ensemble of bacteria attached to a microbead. Phys Rev E 84:061908.  https://doi.org/10.1103/PhysRevE.84.061908 CrossRefGoogle Scholar
  20. 20.
    Sahari A, Headen D, Behkam B (2012) Effect of body shape on the motile behavior of bacteria-powered swimming microrobots (BacteriaBots). Biomed Microdevices 14:999–1007.  https://doi.org/10.1007/s10544-012-9712-1 CrossRefGoogle Scholar
  21. 21.
    Sahari A, Traore MA, Scharf BE, Behkam B (2014) Directed transport of bacteria-based drug delivery vehicles: bacterial chemotaxis dominates particle shape. Biomed Microdevices 16:717–725.  https://doi.org/10.1007/s10544-014-9876-y CrossRefGoogle Scholar
  22. 22.
    Tran TH, Hyung Kim D, Kim J et al (2011) Use of an AC electric field in galvanotactic on/off switching of the motion of a microstructure blotted by Serratia marcescens. Appl Phys Lett 99:2009–2012.  https://doi.org/10.1063/1.3624834 CrossRefGoogle Scholar
  23. 23.
    Zhuang J, Carlsen RW, Sitti M (2015) pH-taxis of biohybrid microsystems. Sci Rep 5:1–13.  https://doi.org/10.1038/srep11403 Google Scholar
  24. 24.
    Steager EB, Wong D, Mishra D, et al (2014) Sensors for micro bio robots via synthetic biology. Proc - IEEE Int Conf Robot Autom 3783–3788.  https://doi.org/10.1109/ICRA.2014.6907407
  25. 25.
    West SA, Winzer K, Gardner A, Diggle SPP (2012) Quorum sensing and the confusion about diffusion. Trends Microbiol 20:586–594.  https://doi.org/10.1016/j.tim.2012.09.004 CrossRefGoogle Scholar
  26. 26.
    Ravichandar JD, Bower AG, Julius AA, Collins CH (2017) Transcriptional control of motility enables directional movement of Escherichia coli in a signal gradient. Sci Rep 7:1–14.  https://doi.org/10.1038/s41598-017-08870-6 CrossRefGoogle Scholar
  27. 27.
    Swofford CA, Van Dessel N, Forbes NS (2015) Quorum-sensing Salmonella selectively trigger protein expression within tumors. Proc Natl Acad Sci U S A 112:3457–3462.  https://doi.org/10.1073/pnas.1414558112 CrossRefGoogle Scholar
  28. 28.
    Din MO, Danino T, Prindle A et al (2016) Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536:81–85.  https://doi.org/10.1038/nature18930 CrossRefGoogle Scholar
  29. 29.
    Leaman EJ, Geuther BQ, Behkam B (2018) Quantitative investigation of the role of intra-/intercellular dynamics in bacterial quorum sensing. ACS Synth Biol 7:1030–1042.  https://doi.org/10.1021/acssynbio.7b00406 CrossRefGoogle Scholar
  30. 30.
    Traore MA, Damico CM, Behkam B (2014) Biomanufacturing and self-propulsion dynamics of nanoscale bacteria-enabled autonomous delivery systems. Appl Phys Lett 105:173702.  https://doi.org/10.1063/1.4900641 CrossRefGoogle Scholar
  31. 31.
    Traore MA, Sahari A, Behkam B (2018) Construction of Bacteria-based cargo carriers for targeted Cancer therapy. In: Sirianni R, Behkam B (eds) Targeted drug delivery methods. Humana/Springer Press, New York, pp 25–35CrossRefGoogle Scholar
  32. 32.
    Leaman EJ, Suh S, Behkam B (2018) Nanoscale Bacteria-enabled autonomous drug delivery systems (NanoBEADS) for Cancer therapy. In: Ferreira A, Desai JP (eds) Micro and Nano Robotics in Medicine. World Scientific Publishing, pp 87–109Google Scholar
  33. 33.
    Suh S, Traore MA, Behkam B (2016) Bacterial chemotaxis enabled autonomous sorting of nanoparticles of comparable sizes. Lab Chip 16:1254–1260.  https://doi.org/10.1039/C6LC00059B CrossRefGoogle Scholar
  34. 34.
    Arabagi V, Behkam B, Cheung E, Sitti M (2011) Modeling of stochastic motion of bacteria propelled spherical microbeads. J Appl Phys 109:114702.  https://doi.org/10.1063/1.3592970 CrossRefGoogle Scholar
  35. 35.
    Rivero MA, Tranquillo RT, Buettner HM, Lauffenburger DA (1989) Transport models for chemotactic cell populations based on individual cell behavior. Chem Eng Sci 44:2881–2897.  https://doi.org/10.1016/0009-2509(89)85098-5 CrossRefGoogle Scholar
  36. 36.
    Berg HC, Brown DA (1972) Chemotaxis in Escherichia coli analysed by three-dimensional tracking. Nature 239:500–504.  https://doi.org/10.1038/239500a0 CrossRefGoogle Scholar
  37. 37.
    Whitehead NA, Barnard AM, Slater H et al (2001) Quorum-sensing in gram-negative bacteria. FEMS Microbiol Rev 25:365–404.  https://doi.org/10.1111/j.1574-6976.2001.tb00583.x CrossRefGoogle Scholar
  38. 38.
    Müller J, Kuttler C, Hense BA et al (2006) Cell-cell communication by quorum sensing and dimension-reduction. J Math Biol 53:672–702.  https://doi.org/10.1007/s00285-006-0024-z MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Hense BA, Müller J, Kuttler C, Hartmann A (2012) Spatial heterogeneity of autoinducer regulation systems. Sensors 12:4156–4171.  https://doi.org/10.3390/s120404156 CrossRefGoogle Scholar
  40. 40.
    Iizuka R, Yamagishi-Shirasaki M, Funatsu T (2011) Kinetic study of de novo chromophore maturation of fluorescent proteins. Anal Biochem 414:173–178.  https://doi.org/10.1016/j.ab.2011.03.036 CrossRefGoogle Scholar
  41. 41.
    Kim YI, Burton RE, Burton BM et al (2000) Dynamics of substrate denaturation and translocation by the ClpXP degradation machine. Mol Cell 5:639–648.  https://doi.org/10.1016/S1097-2765(00)80243-9 CrossRefGoogle Scholar
  42. 42.
    Baker TA, Sauer RT (2012) ClpXP, an ATP-powered unfolding and protein-degradation machine. Biochim Biophys Acta, Mol Cell Res 1823:15–28.  https://doi.org/10.1016/j.bbamcr.2011.06.007 CrossRefGoogle Scholar
  43. 43.
    Stewart PS (2003) Diffusion in biofilms. J Bacteriol 185:1485–1491.  https://doi.org/10.1128/JB.185.5.1485 CrossRefGoogle Scholar
  44. 44.
    Kaufmann GF, Sartorio R, Lee S-H et al (2005) Revisiting quorum sensing: discovery of additional chemical and biological functions for 3-oxo-N-acylhomoserine lactones. Proc Natl Acad Sci U S A 102:309–314.  https://doi.org/10.1073/pnas.0408639102 CrossRefGoogle Scholar
  45. 45.
    Salis HM, Mirsky EA, Voigt CA (2009) Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol 27:946–950.  https://doi.org/10.1038/nbt.1568 CrossRefGoogle Scholar
  46. 46.
    Espah Borujeni A, Channarasappa AS, Salis HM (2014) Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites. Nucleic Acids Res 42:2646–2659.  https://doi.org/10.1093/nar/gkt1139 CrossRefGoogle Scholar
  47. 47.
    Leveau JHJ, Lindow SE (2001) Predictive and interpretive simulation of green fluorescent protein expression in reporter Bacteria. J Bacteriol 183:6752–6762.  https://doi.org/10.1128/JB.183.23.6752 CrossRefGoogle Scholar
  48. 48.
    Drescher K, Dunkel J, Cisneros LH et al (2011) Fluid dynamics and noise in bacterial cell – cell and cell – surface scattering. Proc Natl Acad Sci U S A 108:10940–10945.  https://doi.org/10.1073/pnas.1019079108 CrossRefGoogle Scholar
  49. 49.
    Anderson J (1995) Computational fluid dynamics, 1st edn. McGraw-Hill, New YorkGoogle Scholar
  50. 50.
    Traore MA, Behkam B (2013) A PEG-DA microfluidic device for chemotaxis studies. J Micromech Microeng 23:085014.  https://doi.org/10.1088/0960-1317/23/8/085014 CrossRefGoogle Scholar
  51. 51.
    Englert DL, Manson MD, Jayaraman A (2010) Investigation of bacterial chemotaxis in flow-based microfluidic devices. Nat Protoc 5:864–872.  https://doi.org/10.1038/nprot.2010.18 CrossRefGoogle Scholar
  52. 52.
    Dewhirst MW, Secomb TW (2017) Transport of drugs from blood vessels to tumour tissue. Nat Rev Cancer 17:738–750.  https://doi.org/10.1038/nrc.2017.93 CrossRefGoogle Scholar
  53. 53.
    Prindle A, Selimkhanov J, Danino T et al (2012) Genetic circuits in Salmonella typhimurium. ACS Synth Biol 1:458–464.  https://doi.org/10.1021/sb300060e CrossRefGoogle Scholar
  54. 54.
    Zhou S, Gravekamp C, Bermudes D, Liu K (2018) Tumour-targeting bacteria engineered to fight cancer. Nat Rev Cancer 1.  https://doi.org/10.1038/s41568-018-0070-z

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Virginia TechBlacksburgUSA
  2. 2.The Jackson LaboratoryBar HarborUSA
  3. 3.School of Biomedical Engineering and Sciences, Virginia TechBlacksburgUSA
  4. 4.Macromolecules Innovation Institute, Virginia TechBlacksburgUSA

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